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AIDE: Accelerating image-based ecological surveys with interactive machine learning
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DOI：10.1111/2041-210X.13489.png)
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摘要
En 中文
Ecological surveys increasingly rely on large-scale image datasets, typically terabytes of imagery for a single survey. The ability to collect this volume of data allows surveys of unprecedented scale, at the cost of expansive volumes of photo-interpretation labour. We present Annotation Interface for Data-driven Ecology (AIDE), an open-source web framework designed to alleviate the task of image annotation for ecological surveys. AIDE employs an easy-to-use and customisable labelling interface that supports multiple users, database storage and scalability to the cloud and/or multiple machines. Moreover, AIDE closely integrates users and machine learning models into a feedback loop, where user-provided annotations are employed to re-train the model, and the latter is applied over unlabelled images to e.g. identify wildlife. These predictions are then presented to the users in optimised order, according to a customisable active learning criterion. AIDE has a number of deep learning models built-in, but also accepts custom model implementations. Annotation Interface for Data-driven Ecology has the potential to greatly accelerate annotation tasks for a wide range of researches employing image data. AIDE is open-source and can be downloaded for free at .
Keyword:
applied ecology
conservation
monitoring (population ecology)
population ecology
statistics
surveys
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